AI Domains

An AI-based recognition system may compare a face or another credential with authorised records to assist access verification. Such systems must be tested carefully because recognition errors and privacy concerns can create risks. Nevertheless, verifying entry through face recognition is the only security-supporting option; ignoring errors, sharing private data, or unauthorised opening is unsafe.

The system can study signals such as songs played, skipped, replayed, or liked and detect patterns in a user’s interests. It then uses those patterns to personalise later recommendations. A fixed list, alphabetical selection, or random choice does not adapt to individual behaviour, so none of those alternatives explains improvement based on preferences.

A spam filter can examine words, sender details, links, and other features, then classify a message as spam or non-spam using learned patterns or rules. This is a data-based classification task associated with AI. Photocopying, pressing a switch, and printing page numbers follow direct physical or fixed procedures without such pattern-based classification.

Decision support means that AI analyses relevant data and provides summaries, patterns, predictions, or recommendations to help a person make an informed choice. It does not necessarily make or implement the final decision itself. Hiding options, changing decisions without information, or applying every result without human review describes poor or unsafe practice, not decision support.

Automation allows a system to perform suitable routine or repetitive steps with limited manual intervention. When designed and monitored properly, this can save time, reduce repetitive effort, and improve consistency. Automation does not guarantee that every result is wrong or that human effort always increases, and privacy violations are a risk to prevent, not a benefit.

Image recognition involves examining visual features such as shapes, colours, and textures and matching them with learned patterns to identify an object or animal. That is a common computer-vision application of AI. Pushing a chair, wetting paper, and placing a clock are physical actions; none requires interpreting image patterns to identify something.

A spam classifier can learn patterns from examples, such as wording, sender behaviour, links, or message structure, and apply them to new emails. It then predicts whether a message is spam or not. A calculator following predetermined arithmetic steps and a manually operated switch perform fixed actions; merely reading rules without analysing data is not this kind of AI classification.

The governing concept is a complete pictograph key or legend. If one full symbol equals 10 students, a half symbol may represent 5 students, but that interpretation should be stated rather than assumed. Without the partial-symbol value, readers may calculate different totals or compare categories incorrectly. The issue is not colour, decoration, or image size; it is ambiguity in the data explanation. Thus option C is correct.